ml-finance-python
python scripts for finance machine learning
git clone https://9o.is/git/ml-finance-python.git
notebook.ipynb
(131566B)
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9 "# CBOE VXN Index\n",
10 "\n",
11 "In this notebook, we'll take a look at the CBOE VXN Index dataset, available on the [Quantopian Store](https://www.quantopian.com/store). This dataset spans 02 Feb 2001 through the current day. This data has a daily frequency. CBOE VXN measures market expectations of near-term volatility conveyed by NASDAQ-100 Index option prices\n",
12 "\n",
13 "## Notebook Contents\n",
14 "\n",
15 "There are two ways to access the data and you'll find both of them listed below. Just click on the section you'd like to read through.\n",
16 "\n",
17 "- <a href='#interactive'><strong>Interactive overview</strong></a>: This is only available on Research and uses blaze to give you access to large amounts of data. Recommended for exploration and plotting.\n",
18 "- <a href='#pipeline'><strong>Pipeline overview</strong></a>: Data is made available through pipeline which is available on both the Research & Backtesting environment. Recommended for custom factor development and moving back & forth between research/backtesting.\n",
19 "\n",
20 "\n",
21 "### Limits\n",
22 "One key caveat: we limit the number of results returned from any given expression to 10,000 to protect against runaway memory usage. To be clear, you have access to all the data server side. We are limiting the size of the responses back from Blaze.\n",
23 "\n",
24 "With preamble in place, let's get started:\n",
25 "\n",
26 "<a id='interactive'></a>\n",
27 "#Interactive Overview\n",
28 "### Accessing the data with Blaze and Interactive on Research\n",
29 "Partner datasets are available on Quantopian Research through an API service known as [Blaze](http://blaze.pydata.org). Blaze provides the Quantopian user with a convenient interface to access very large datasets, in an interactive, generic manner.\n",
30 "\n",
31 "Blaze provides an important function for accessing these datasets. Some of these sets are many millions of records. Bringing that data directly into Quantopian Research directly just is not viable. So Blaze allows us to provide a simple querying interface and shift the burden over to the server side.\n",
32 "\n",
33 "It is common to use Blaze to reduce your dataset in size, convert it over to Pandas and then to use Pandas for further computation, manipulation and visualization.\n",
34 "\n",
35 "Helpful links:\n",
36 "* [Query building for Blaze](http://blaze.readthedocs.io/en/latest/queries.html)\n",
37 "* [Pandas-to-Blaze dictionary](http://blaze.readthedocs.io/en/latest/rosetta-pandas.html)\n",
38 "* [SQL-to-Blaze dictionary](http://blaze.readthedocs.io/en/latest/rosetta-sql.html).\n",
39 "\n",
40 "\n",
41 "Once you've limited the size of your Blaze object, you can convert it to a Pandas DataFrames using:\n",
42 "> `from odo import odo` \n",
43 "> `odo(expr, pandas.DataFrame)`\n",
44 "\n",
45 "\n",
46 "###To see how this data can be used in your algorithm, search for the `Pipeline Overview` section of this notebook or head straight to <a href='#pipeline'>Pipeline Overview</a>"
47 ]
48 },
49 {
50 "cell_type": "code",
51 "execution_count": 2,
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56 "source": [
57 "# For use in Quantopian Research, exploring interactively\n",
58 "from quantopian.interactive.data.quandl import cboe_vxn as dataset\n",
59 "\n",
60 "# import data operations\n",
61 "from odo import odo\n",
62 "# import other libraries we will use\n",
63 "import pandas as pd"
64 ]
65 },
66 {
67 "cell_type": "code",
68 "execution_count": 3,
69 "metadata": {
70 "collapsed": false
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76 "dshape(\"\"\"var * {\n",
77 " open_: float64,\n",
78 " high: float64,\n",
79 " low: float64,\n",
80 " close: float64,\n",
81 " asof_date: datetime,\n",
82 " timestamp: datetime\n",
83 " }\"\"\")"
84 ]
85 },
86 "execution_count": 3,
87 "metadata": {},
88 "output_type": "execute_result"
89 }
90 ],
91 "source": [
92 "# Let's use blaze to understand the data a bit using Blaze dshape()\n",
93 "dataset.dshape"
94 ]
95 },
96 {
97 "cell_type": "code",
98 "execution_count": 4,
99 "metadata": {
100 "collapsed": false
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106 "3848"
107 ],
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109 "3848"
110 ]
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114 "output_type": "execute_result"
115 }
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117 "source": [
118 "# And how many rows are there?\n",
119 "# N.B. we're using a Blaze function to do this, not len()\n",
120 "dataset.count()"
121 ]
122 },
123 {
124 "cell_type": "code",
125 "execution_count": 5,
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133 "<table border=\"1\" class=\"dataframe\">\n",
134 " <thead>\n",
135 " <tr style=\"text-align: right;\">\n",
136 " <th></th>\n",
137 " <th>open_</th>\n",
138 " <th>high</th>\n",
139 " <th>low</th>\n",
140 " <th>close</th>\n",
141 " <th>asof_date</th>\n",
142 " <th>timestamp</th>\n",
143 " </tr>\n",
144 " </thead>\n",
145 " <tbody>\n",
146 " <tr>\n",
147 " <th>0</th>\n",
148 " <td>24.31</td>\n",
149 " <td>25.45</td>\n",
150 " <td>23.93</td>\n",
151 " <td>25.30</td>\n",
152 " <td>2016-02-23</td>\n",
153 " <td>2016-02-24 12:03:01.367088</td>\n",
154 " </tr>\n",
155 " <tr>\n",
156 " <th>1</th>\n",
157 " <td>26.75</td>\n",
158 " <td>27.00</td>\n",
159 " <td>23.95</td>\n",
160 " <td>24.00</td>\n",
161 " <td>2016-02-24</td>\n",
162 " <td>2016-02-25 12:03:35.148458</td>\n",
163 " </tr>\n",
164 " <tr>\n",
165 " <th>2</th>\n",
166 " <td>24.00</td>\n",
167 " <td>25.59</td>\n",
168 " <td>22.62</td>\n",
169 " <td>22.62</td>\n",
170 " <td>2016-02-25</td>\n",
171 " <td>2016-02-26 12:01:34.448860</td>\n",
172 " </tr>\n",
173 " </tbody>\n",
174 "</table>"
175 ],
176 "text/plain": [
177 " open_ high low close asof_date timestamp\n",
178 "0 24.31 25.45 23.93 25.30 2016-02-23 2016-02-24 12:03:01.367088\n",
179 "1 26.75 27.00 23.95 24.00 2016-02-24 2016-02-25 12:03:35.148458\n",
180 "2 24.00 25.59 22.62 22.62 2016-02-25 2016-02-26 12:01:34.448860"
181 ]
182 },
183 "execution_count": 5,
184 "metadata": {},
185 "output_type": "execute_result"
186 }
187 ],
188 "source": [
189 "# Let's see what the data looks like. We'll grab the first three rows.\n",
190 "dataset[:3]"
191 ]
192 },
193 {
194 "cell_type": "markdown",
195 "metadata": {},
196 "source": [
197 "Let's go over the columns:\n",
198 "- **open**: open price for VXN\n",
199 "- **high**: daily high for VXN\n",
200 "- **low**: daily low for VXN\n",
201 "- **close**: close price for VXN\n",
202 "- **asof_date**: the timeframe to which this data applies\n",
203 "- **timestamp**: this is our timestamp on when we registered the data.\n",
204 "\n",
205 "We've done much of the data processing for you. Fields like `timestamp` are standardized across all our Store Datasets, so the datasets are easy to combine.\n",
206 "\n",
207 "We can select columns and rows with ease. Below, we'll do a simple plot."
208 ]
209 },
210 {
211 "cell_type": "code",
212 "execution_count": 6,
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220 "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
221 "<table border=\"1\" class=\"dataframe\">\n",
222 " <thead>\n",
223 " <tr style=\"text-align: right;\">\n",
224 " <th></th>\n",
225 " <th>open_</th>\n",
226 " <th>high</th>\n",
227 " <th>low</th>\n",
228 " <th>close</th>\n",
229 " <th>asof_date</th>\n",
230 " <th>timestamp</th>\n",
231 " </tr>\n",
232 " </thead>\n",
233 " <tbody>\n",
234 " <tr>\n",
235 " <th>0</th>\n",
236 " <td>24.31</td>\n",
237 " <td>25.45</td>\n",
238 " <td>23.93</td>\n",
239 " <td>25.30</td>\n",
240 " <td>2016-02-23</td>\n",
241 " <td>2016-02-24 12:03:01.367088</td>\n",
242 " </tr>\n",
243 " <tr>\n",
244 " <th>1</th>\n",
245 " <td>26.75</td>\n",
246 " <td>27.00</td>\n",
247 " <td>23.95</td>\n",
248 " <td>24.00</td>\n",
249 " <td>2016-02-24</td>\n",
250 " <td>2016-02-25 12:03:35.148458</td>\n",
251 " </tr>\n",
252 " <tr>\n",
253 " <th>2</th>\n",
254 " <td>24.00</td>\n",
255 " <td>25.59</td>\n",
256 " <td>22.62</td>\n",
257 " <td>22.62</td>\n",
258 " <td>2016-02-25</td>\n",
259 " <td>2016-02-26 12:01:34.448860</td>\n",
260 " </tr>\n",
261 " <tr>\n",
262 " <th>3</th>\n",
263 " <td>22.60</td>\n",
264 " <td>23.12</td>\n",
265 " <td>21.88</td>\n",
266 " <td>23.08</td>\n",
267 " <td>2016-02-26</td>\n",
268 " <td>2016-02-29 12:03:37.230125</td>\n",
269 " </tr>\n",
270 " <tr>\n",
271 " <th>4</th>\n",
272 " <td>23.72</td>\n",
273 " <td>23.79</td>\n",
274 " <td>22.34</td>\n",
275 " <td>23.55</td>\n",
276 " <td>2016-02-29</td>\n",
277 " <td>2016-03-01 12:01:52.881415</td>\n",
278 " </tr>\n",
279 " </tbody>\n",
280 "</table>\n",
281 "</div>"
282 ],
283 "text/plain": [
284 " open_ high low close asof_date timestamp\n",
285 "0 24.31 25.45 23.93 25.30 2016-02-23 2016-02-24 12:03:01.367088\n",
286 "1 26.75 27.00 23.95 24.00 2016-02-24 2016-02-25 12:03:35.148458\n",
287 "2 24.00 25.59 22.62 22.62 2016-02-25 2016-02-26 12:01:34.448860\n",
288 "3 22.60 23.12 21.88 23.08 2016-02-26 2016-02-29 12:03:37.230125\n",
289 "4 23.72 23.79 22.34 23.55 2016-02-29 2016-03-01 12:01:52.881415"
290 ]
291 },
292 "execution_count": 6,
293 "metadata": {},
294 "output_type": "execute_result"
295 }
296 ],
297 "source": [
298 "# Plotting this DataFrame\n",
299 "df = odo(dataset, pd.DataFrame)\n",
300 "df.head(5)"
301 ]
302 },
303 {
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305 "execution_count": 7,
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313 "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
314 "<table border=\"1\" class=\"dataframe\">\n",
315 " <thead>\n",
316 " <tr style=\"text-align: right;\">\n",
317 " <th></th>\n",
318 " <th>open_</th>\n",
319 " <th>high</th>\n",
320 " <th>low</th>\n",
321 " <th>close</th>\n",
322 " <th>timestamp</th>\n",
323 " </tr>\n",
324 " <tr>\n",
325 " <th>asof_date</th>\n",
326 " <th></th>\n",
327 " <th></th>\n",
328 " <th></th>\n",
329 " <th></th>\n",
330 " <th></th>\n",
331 " </tr>\n",
332 " </thead>\n",
333 " <tbody>\n",
334 " <tr>\n",
335 " <th>2016-02-23</th>\n",
336 " <td>24.31</td>\n",
337 " <td>25.45</td>\n",
338 " <td>23.93</td>\n",
339 " <td>25.30</td>\n",
340 " <td>2016-02-24 12:03:01.367088</td>\n",
341 " </tr>\n",
342 " <tr>\n",
343 " <th>2016-02-24</th>\n",
344 " <td>26.75</td>\n",
345 " <td>27.00</td>\n",
346 " <td>23.95</td>\n",
347 " <td>24.00</td>\n",
348 " <td>2016-02-25 12:03:35.148458</td>\n",
349 " </tr>\n",
350 " <tr>\n",
351 " <th>2016-02-25</th>\n",
352 " <td>24.00</td>\n",
353 " <td>25.59</td>\n",
354 " <td>22.62</td>\n",
355 " <td>22.62</td>\n",
356 " <td>2016-02-26 12:01:34.448860</td>\n",
357 " </tr>\n",
358 " <tr>\n",
359 " <th>2016-02-26</th>\n",
360 " <td>22.60</td>\n",
361 " <td>23.12</td>\n",
362 " <td>21.88</td>\n",
363 " <td>23.08</td>\n",
364 " <td>2016-02-29 12:03:37.230125</td>\n",
365 " </tr>\n",
366 " <tr>\n",
367 " <th>2016-02-29</th>\n",
368 " <td>23.72</td>\n",
369 " <td>23.79</td>\n",
370 " <td>22.34</td>\n",
371 " <td>23.55</td>\n",
372 " <td>2016-03-01 12:01:52.881415</td>\n",
373 " </tr>\n",
374 " </tbody>\n",
375 "</table>\n",
376 "</div>"
377 ],
378 "text/plain": [
379 " open_ high low close timestamp\n",
380 "asof_date \n",
381 "2016-02-23 24.31 25.45 23.93 25.30 2016-02-24 12:03:01.367088\n",
382 "2016-02-24 26.75 27.00 23.95 24.00 2016-02-25 12:03:35.148458\n",
383 "2016-02-25 24.00 25.59 22.62 22.62 2016-02-26 12:01:34.448860\n",
384 "2016-02-26 22.60 23.12 21.88 23.08 2016-02-29 12:03:37.230125\n",
385 "2016-02-29 23.72 23.79 22.34 23.55 2016-03-01 12:01:52.881415"
386 ]
387 },
388 "execution_count": 7,
389 "metadata": {},
390 "output_type": "execute_result"
391 }
392 ],
393 "source": [
394 "# So we can plot it, we'll set the index as the `asof_date`\n",
395 "df['asof_date'] = pd.to_datetime(df['asof_date'])\n",
396 "df = df.set_index(['asof_date'])\n",
397 "df.head(5)"
398 ]
399 },
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du72OO/C0bU8Mw9All1yi9evXKysrS3a7Xe+99165x/AEur/LLrvMGmOye/dubdy40Xqs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\nlXqDUxQi7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tQ3tC4kcbnZGoVGeETkgkKCLfxiZkaH3wSJiMjVpanNMssW8+XfxPeNj+Ld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\ni4txOBzU1dV5/u21115j27Ztm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420 "text/plain": [
421 "<matplotlib.figure.Figure at 0x7f53329749d0>"
422 ]
423 },
424 "metadata": {},
425 "output_type": "display_data"
426 }
427 ],
428 "source": [
429 "import matplotlib.pyplot as plt\n",
430 "df['open_'].plot(label=str(dataset))\n",
431 "plt.ylabel(str(dataset))\n",
432 "plt.legend()\n",
433 "plt.title(\"Graphing %s since %s\" % (str(dataset), min(df.index)))"
434 ]
435 },
436 {
437 "cell_type": "markdown",
438 "metadata": {},
439 "source": [
440 "<a id='pipeline'></a>\n",
441 "\n",
442 "#Pipeline Overview\n",
443 "\n",
444 "### Accessing the data in your algorithms & research\n",
445 "The only method for accessing partner data within algorithms running on Quantopian is via the pipeline API. Different data sets work differently but in the case of this data, you can add this data to your pipeline as follows:\n",
446 "\n",
447 "Import the data set here\n",
448 "> `from quantopian.pipeline.data.quandl import cboe_vxn`\n",
449 "\n",
450 "Then in intialize() you could do something simple like adding the raw value of one of the fields to your pipeline:\n",
451 "> `pipe.add(cboe_vxn.open_.latest, 'open')`\n",
452 "\n",
453 "Pipeline usage is very similar between the backtester and Research so let's go over how to import this data through pipeline and view its outputs."
454 ]
455 },
456 {
457 "cell_type": "code",
458 "execution_count": 9,
459 "metadata": {
460 "collapsed": true
461 },
462 "outputs": [],
463 "source": [
464 "# Import necessary Pipeline modules\n",
465 "from quantopian.pipeline import Pipeline\n",
466 "from quantopian.research import run_pipeline\n",
467 "from quantopian.pipeline.factors import AverageDollarVolume"
468 ]
469 },
470 {
471 "cell_type": "code",
472 "execution_count": 1,
473 "metadata": {
474 "collapsed": false
475 },
476 "outputs": [],
477 "source": [
478 "# Import the datasets available\n",
479 "from quantopian.pipeline.data.quandl import cboe_vxn"
480 ]
481 },
482 {
483 "cell_type": "markdown",
484 "metadata": {},
485 "source": [
486 "Now that we've imported the data, let's take a look at which fields are available for each dataset.\n",
487 "\n",
488 "You'll find the dataset, the available fields, and the datatypes for each of those fields."
489 ]
490 },
491 {
492 "cell_type": "code",
493 "execution_count": 10,
494 "metadata": {
495 "collapsed": false
496 },
497 "outputs": [
498 {
499 "name": "stdout",
500 "output_type": "stream",
501 "text": [
502 "Here are the list of available fields per dataset:\n",
503 "---------------------------------------------------\n",
504 "\n",
505 "Dataset: cboe_vxn\n",
506 "\n",
507 "Fields:\n",
508 "open_ - float64\n",
509 "low - float64\n",
510 "high - float64\n",
511 "close - float64\n",
512 "\n",
513 "\n",
514 "---------------------------------------------------\n",
515 "\n"
516 ]
517 }
518 ],
519 "source": [
520 "print \"Here are the list of available fields per dataset:\"\n",
521 "print \"---------------------------------------------------\\n\"\n",
522 "\n",
523 "def _print_fields(dataset):\n",
524 " print \"Dataset: %s\\n\" % dataset.__name__\n",
525 " print \"Fields:\"\n",
526 " for field in list(dataset.columns):\n",
527 " print \"%s - %s\" % (field.name, field.dtype)\n",
528 " print \"\\n\"\n",
529 "\n",
530 "_print_fields(cboe_vxn)\n",
531 "\n",
532 "\n",
533 "print \"---------------------------------------------------\\n\""
534 ]
535 },
536 {
537 "cell_type": "markdown",
538 "metadata": {},
539 "source": [
540 "Now that we know what fields we have access to, let's see what this data looks like when we run it through Pipeline.\n",
541 "\n",
542 "\n",
543 "This is constructed the same way as you would in the backtester. For more information on using Pipeline in Research view this thread:\n",
544 "https://www.quantopian.com/posts/pipeline-in-research-build-test-and-visualize-your-factors-and-filters"
545 ]
546 },
547 {
548 "cell_type": "code",
549 "execution_count": 11,
550 "metadata": {
551 "collapsed": false
552 },
553 "outputs": [],
554 "source": [
555 "pipe = Pipeline()\n",
556 " \n",
557 "pipe.add(cboe_vxn.open_.latest, 'open_vxn')"
558 ]
559 },
560 {
561 "cell_type": "code",
562 "execution_count": 13,
563 "metadata": {
564 "collapsed": false
565 },
566 "outputs": [],
567 "source": [
568 "# Setting some basic liquidity strings (just for good habit)\n",
569 "dollar_volume = AverageDollarVolume(window_length=20)\n",
570 "top_1000_most_liquid = dollar_volume.rank(ascending=False) < 1000\n",
571 "\n",
572 "pipe.set_screen(top_1000_most_liquid & cboe_vxn.open_.latest.notnan())"
573 ]
574 },
575 {
576 "cell_type": "code",
577 "execution_count": 14,
578 "metadata": {
579 "collapsed": false
580 },
581 "outputs": [
582 {
583 "data": {
584 "image/png": 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bW97e3jxyBgAApAmBEngAdO3aVT4+PoqNjVW/fv2cXQ7cVJ8+fRQdHa3o6GhO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\nX5cVMKqPfOpXV/Yc2RP6eOHsRc0NtNzH9m+2Vck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585 "text/plain": [
586 "<IPython.core.display.Image object>"
587 ]
588 },
589 "execution_count": 14,
590 "metadata": {},
591 "output_type": "execute_result"
592 }
593 ],
594 "source": [
595 "# The show_graph() method of pipeline objects produces a graph to show how it is being calculated.\n",
596 "pipe.show_graph(format='png')"
597 ]
598 },
599 {
600 "cell_type": "code",
601 "execution_count": 15,
602 "metadata": {
603 "collapsed": false
604 },
605 "outputs": [
606 {
607 "data": {
608 "text/html": [
609 "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
610 "<table border=\"1\" class=\"dataframe\">\n",
611 " <thead>\n",
612 " <tr style=\"text-align: right;\">\n",
613 " <th></th>\n",
614 " <th></th>\n",
615 " <th>open_vxn</th>\n",
616 " </tr>\n",
617 " </thead>\n",
618 " <tbody>\n",
619 " <tr>\n",
620 " <th rowspan=\"30\" valign=\"top\">2013-11-01 00:00:00+00:00</th>\n",
621 " <th>Equity(21 [AAME])</th>\n",
622 " <td>14.83</td>\n",
623 " </tr>\n",
624 " <tr>\n",
625 " <th>Equity(25 [AA_PR])</th>\n",
626 " <td>14.83</td>\n",
627 " </tr>\n",
628 " <tr>\n",
629 " <th>Equity(117 [AEY])</th>\n",
630 " <td>14.83</td>\n",
631 " </tr>\n",
632 " <tr>\n",
633 " <th>Equity(225 [AHPI])</th>\n",
634 " <td>14.83</td>\n",
635 " </tr>\n",
636 " <tr>\n",
637 " <th>Equity(312 [ALOT])</th>\n",
638 " <td>14.83</td>\n",
639 " </tr>\n",
640 " <tr>\n",
641 " <th>Equity(392 [AMS])</th>\n",
642 " <td>14.83</td>\n",
643 " </tr>\n",
644 " <tr>\n",
645 " <th>Equity(468 [API])</th>\n",
646 " <td>14.83</td>\n",
647 " </tr>\n",
648 " <tr>\n",
649 " <th>Equity(548 [ASBI])</th>\n",
650 " <td>14.83</td>\n",
651 " </tr>\n",
652 " <tr>\n",
653 " <th>Equity(717 [BAMM])</th>\n",
654 " <td>14.83</td>\n",
655 " </tr>\n",
656 " <tr>\n",
657 " <th>Equity(790 [BDL])</th>\n",
658 " <td>14.83</td>\n",
659 " </tr>\n",
660 " <tr>\n",
661 " <th>Equity(880 [BIO_B])</th>\n",
662 " <td>14.83</td>\n",
663 " </tr>\n",
664 " <tr>\n",
665 " <th>Equity(925 [BKSC])</th>\n",
666 " <td>14.83</td>\n",
667 " </tr>\n",
668 " <tr>\n",
669 " <th>Equity(1088 [BRID])</th>\n",
670 " <td>14.83</td>\n",
671 " </tr>\n",
672 " <tr>\n",
673 " <th>Equity(1095 [BRN])</th>\n",
674 " <td>14.83</td>\n",
675 " </tr>\n",
676 " <tr>\n",
677 " <th>Equity(1157 [BTUI])</th>\n",
678 " <td>14.83</td>\n",
679 " </tr>\n",
680 " <tr>\n",
681 " <th>Equity(1190 [BWIN_A])</th>\n",
682 " <td>14.83</td>\n",
683 " </tr>\n",
684 " <tr>\n",
685 " <th>Equity(1193 [BWL_A])</th>\n",
686 " <td>14.83</td>\n",
687 " </tr>\n",
688 " <tr>\n",
689 " <th>Equity(1323 [CAW])</th>\n",
690 " <td>14.83</td>\n",
691 " </tr>\n",
692 " <tr>\n",
693 " <th>Equity(1653 [MOC])</th>\n",
694 " <td>14.83</td>\n",
695 " </tr>\n",
696 " <tr>\n",
697 " <th>Equity(1668 [CMS_PRB])</th>\n",
698 " <td>14.83</td>\n",
699 " </tr>\n",
700 " <tr>\n",
701 " <th>Equity(1988 [CUO])</th>\n",
702 " <td>14.83</td>\n",
703 " </tr>\n",
704 " <tr>\n",
705 " <th>Equity(2078 [DAIO])</th>\n",
706 " <td>14.83</td>\n",
707 " </tr>\n",
708 " <tr>\n",
709 " <th>Equity(2103 [ESCR])</th>\n",
710 " <td>14.83</td>\n",
711 " </tr>\n",
712 " <tr>\n",
713 " <th>Equity(2124 [DD_PRA])</th>\n",
714 " <td>14.83</td>\n",
715 " </tr>\n",
716 " <tr>\n",
717 " <th>Equity(2209 [DGSE])</th>\n",
718 " <td>14.83</td>\n",
719 " </tr>\n",
720 " <tr>\n",
721 " <th>Equity(2292 [DRCO])</th>\n",
722 " <td>14.83</td>\n",
723 " </tr>\n",
724 " <tr>\n",
725 " <th>Equity(2344 [DRAM])</th>\n",
726 " <td>14.83</td>\n",
727 " </tr>\n",
728 " <tr>\n",
729 " <th>Equity(2382 [DXR])</th>\n",
730 " <td>14.83</td>\n",
731 " </tr>\n",
732 " <tr>\n",
733 " <th>Equity(2389 [COBR])</th>\n",
734 " <td>14.83</td>\n",
735 " </tr>\n",
736 " <tr>\n",
737 " <th>Equity(2391 [DYNT])</th>\n",
738 " <td>14.83</td>\n",
739 " </tr>\n",
740 " <tr>\n",
741 " <th>...</th>\n",
742 " <th>...</th>\n",
743 " <td>...</td>\n",
744 " </tr>\n",
745 " <tr>\n",
746 " <th rowspan=\"30\" valign=\"top\">2013-11-25 00:00:00+00:00</th>\n",
747 " <th>Equity(45179 [ERW])</th>\n",
748 " <td>14.02</td>\n",
749 " </tr>\n",
750 " <tr>\n",
751 " <th>Equity(45195 [LGL_WS])</th>\n",
752 " <td>14.02</td>\n",
753 " </tr>\n",
754 " <tr>\n",
755 " <th>Equity(45203 [NASH])</th>\n",
756 " <td>14.02</td>\n",
757 " </tr>\n",
758 " <tr>\n",
759 " <th>Equity(45222 [QPAC_U])</th>\n",
760 " <td>14.02</td>\n",
761 " </tr>\n",
762 " <tr>\n",
763 " <th>Equity(45240 [INTL_L])</th>\n",
764 " <td>14.02</td>\n",
765 " </tr>\n",
766 " <tr>\n",
767 " <th>Equity(45270 [TIPT])</th>\n",
768 " <td>14.02</td>\n",
769 " </tr>\n",
770 " <tr>\n",
771 " <th>Equity(45288 [EMHD])</th>\n",
772 " <td>14.02</td>\n",
773 " </tr>\n",
774 " <tr>\n",
775 " <th>Equity(45301 [TRC_WS])</th>\n",
776 " <td>14.02</td>\n",
777 " </tr>\n",
778 " <tr>\n",
779 " <th>Equity(45390 [CPXX])</th>\n",
780 " <td>14.02</td>\n",
781 " </tr>\n",
782 " <tr>\n",
783 " <th>Equity(45412 [EAGL])</th>\n",
784 " <td>14.02</td>\n",
785 " </tr>\n",
786 " <tr>\n",
787 " <th>Equity(45414 [EAGL_W])</th>\n",
788 " <td>14.02</td>\n",
789 " </tr>\n",
790 " <tr>\n",
791 " <th>Equity(45420 [ROIQ_U])</th>\n",
792 " <td>14.02</td>\n",
793 " </tr>\n",
794 " <tr>\n",
795 " <th>Equity(45432 [SPCB])</th>\n",
796 " <td>14.02</td>\n",
797 " </tr>\n",
798 " <tr>\n",
799 " <th>Equity(45510 [MLPC])</th>\n",
800 " <td>14.02</td>\n",
801 " </tr>\n",
802 " <tr>\n",
803 " <th>Equity(45524 [NVEE])</th>\n",
804 " <td>14.02</td>\n",
805 " </tr>\n",
806 " <tr>\n",
807 " <th>Equity(45525 [NVEE_W])</th>\n",
808 " <td>14.02</td>\n",
809 " </tr>\n",
810 " <tr>\n",
811 " <th>Equity(45527 [JASN])</th>\n",
812 " <td>14.02</td>\n",
813 " </tr>\n",
814 " <tr>\n",
815 " <th>Equity(45536 [JASN_W])</th>\n",
816 " <td>14.02</td>\n",
817 " </tr>\n",
818 " <tr>\n",
819 " <th>Equity(45562 [ESBA])</th>\n",
820 " <td>14.02</td>\n",
821 " </tr>\n",
822 " <tr>\n",
823 " <th>Equity(45563 [OGCP])</th>\n",
824 " <td>14.02</td>\n",
825 " </tr>\n",
826 " <tr>\n",
827 " <th>Equity(45564 [FISK])</th>\n",
828 " <td>14.02</td>\n",
829 " </tr>\n",
830 " <tr>\n",
831 " <th>Equity(45646 [CHNA])</th>\n",
832 " <td>14.02</td>\n",
833 " </tr>\n",
834 " <tr>\n",
835 " <th>Equity(45678 [SLQD])</th>\n",
836 " <td>14.02</td>\n",
837 " </tr>\n",
838 " <tr>\n",
839 " <th>Equity(45680 [ADXS_W])</th>\n",
840 " <td>14.02</td>\n",
841 " </tr>\n",
842 " <tr>\n",
843 " <th>Equity(45717 [FTGC])</th>\n",
844 " <td>14.02</td>\n",
845 " </tr>\n",
846 " <tr>\n",
847 " <th>Equity(45768 [KODK_WS])</th>\n",
848 " <td>14.02</td>\n",
849 " </tr>\n",
850 " <tr>\n",
851 " <th>Equity(45792 [FTSD])</th>\n",
852 " <td>14.02</td>\n",
853 " </tr>\n",
854 " <tr>\n",
855 " <th>Equity(45824 [ROIQ_W])</th>\n",
856 " <td>14.02</td>\n",
857 " </tr>\n",
858 " <tr>\n",
859 " <th>Equity(45854 [PGAL])</th>\n",
860 " <td>14.02</td>\n",
861 " </tr>\n",
862 " <tr>\n",
863 " <th>Equity(45895 [EMSH])</th>\n",
864 " <td>14.02</td>\n",
865 " </tr>\n",
866 " </tbody>\n",
867 "</table>\n",
868 "<p>16983 rows × 1 columns</p>\n",
869 "</div>"
870 ],
871 "text/plain": [
872 " open_vxn\n",
873 "2013-11-01 00:00:00+00:00 Equity(21 [AAME]) 14.83\n",
874 " Equity(25 [AA_PR]) 14.83\n",
875 " Equity(117 [AEY]) 14.83\n",
876 " Equity(225 [AHPI]) 14.83\n",
877 " Equity(312 [ALOT]) 14.83\n",
878 " Equity(392 [AMS]) 14.83\n",
879 " Equity(468 [API]) 14.83\n",
880 " Equity(548 [ASBI]) 14.83\n",
881 " Equity(717 [BAMM]) 14.83\n",
882 " Equity(790 [BDL]) 14.83\n",
883 " Equity(880 [BIO_B]) 14.83\n",
884 " Equity(925 [BKSC]) 14.83\n",
885 " Equity(1088 [BRID]) 14.83\n",
886 " Equity(1095 [BRN]) 14.83\n",
887 " Equity(1157 [BTUI]) 14.83\n",
888 " Equity(1190 [BWIN_A]) 14.83\n",
889 " Equity(1193 [BWL_A]) 14.83\n",
890 " Equity(1323 [CAW]) 14.83\n",
891 " Equity(1653 [MOC]) 14.83\n",
892 " Equity(1668 [CMS_PRB]) 14.83\n",
893 " Equity(1988 [CUO]) 14.83\n",
894 " Equity(2078 [DAIO]) 14.83\n",
895 " Equity(2103 [ESCR]) 14.83\n",
896 " Equity(2124 [DD_PRA]) 14.83\n",
897 " Equity(2209 [DGSE]) 14.83\n",
898 " Equity(2292 [DRCO]) 14.83\n",
899 " Equity(2344 [DRAM]) 14.83\n",
900 " Equity(2382 [DXR]) 14.83\n",
901 " Equity(2389 [COBR]) 14.83\n",
902 " Equity(2391 [DYNT]) 14.83\n",
903 "... ...\n",
904 "2013-11-25 00:00:00+00:00 Equity(45179 [ERW]) 14.02\n",
905 " Equity(45195 [LGL_WS]) 14.02\n",
906 " Equity(45203 [NASH]) 14.02\n",
907 " Equity(45222 [QPAC_U]) 14.02\n",
908 " Equity(45240 [INTL_L]) 14.02\n",
909 " Equity(45270 [TIPT]) 14.02\n",
910 " Equity(45288 [EMHD]) 14.02\n",
911 " Equity(45301 [TRC_WS]) 14.02\n",
912 " Equity(45390 [CPXX]) 14.02\n",
913 " Equity(45412 [EAGL]) 14.02\n",
914 " Equity(45414 [EAGL_W]) 14.02\n",
915 " Equity(45420 [ROIQ_U]) 14.02\n",
916 " Equity(45432 [SPCB]) 14.02\n",
917 " Equity(45510 [MLPC]) 14.02\n",
918 " Equity(45524 [NVEE]) 14.02\n",
919 " Equity(45525 [NVEE_W]) 14.02\n",
920 " Equity(45527 [JASN]) 14.02\n",
921 " Equity(45536 [JASN_W]) 14.02\n",
922 " Equity(45562 [ESBA]) 14.02\n",
923 " Equity(45563 [OGCP]) 14.02\n",
924 " Equity(45564 [FISK]) 14.02\n",
925 " Equity(45646 [CHNA]) 14.02\n",
926 " Equity(45678 [SLQD]) 14.02\n",
927 " Equity(45680 [ADXS_W]) 14.02\n",
928 " Equity(45717 [FTGC]) 14.02\n",
929 " Equity(45768 [KODK_WS]) 14.02\n",
930 " Equity(45792 [FTSD]) 14.02\n",
931 " Equity(45824 [ROIQ_W]) 14.02\n",
932 " Equity(45854 [PGAL]) 14.02\n",
933 " Equity(45895 [EMSH]) 14.02\n",
934 "\n",
935 "[16983 rows x 1 columns]"
936 ]
937 },
938 "execution_count": 15,
939 "metadata": {},
940 "output_type": "execute_result"
941 }
942 ],
943 "source": [
944 "# run_pipeline will show the output of your pipeline\n",
945 "pipe_output = run_pipeline(pipe, start_date='2013-11-01', end_date='2013-11-25')\n",
946 "pipe_output"
947 ]
948 },
949 {
950 "cell_type": "markdown",
951 "metadata": {},
952 "source": [
953 "Here, you'll notice that each security is mapped to the corresponding value, so you could grab any security to get what you need.\n",
954 "\n",
955 "Taking what we've seen from above, let's see how we'd move that into the backtester."
956 ]
957 },
958 {
959 "cell_type": "code",
960 "execution_count": 17,
961 "metadata": {
962 "collapsed": false
963 },
964 "outputs": [],
965 "source": [
966 "# This section is only importable in the backtester\n",
967 "from quantopian.algorithm import attach_pipeline, pipeline_output\n",
968 "\n",
969 "# General pipeline imports\n",
970 "from quantopian.pipeline import Pipeline\n",
971 "from quantopian.pipeline.factors import AverageDollarVolume\n",
972 "\n",
973 "# For use in your algorithms via the pipeline API\n",
974 "from quantopian.pipeline.data.quandl import cboe_vxn\n",
975 "\n",
976 "def make_pipeline():\n",
977 " # Create our pipeline\n",
978 " pipe = Pipeline()\n",
979 " \n",
980 " # Screen out penny stocks and low liquidity securities.\n",
981 " dollar_volume = AverageDollarVolume(window_length=20)\n",
982 " is_liquid = dollar_volume.rank(ascending=False) < 1000\n",
983 " \n",
984 " # Create the mask that we will use for our percentile methods.\n",
985 " base_universe = (is_liquid)\n",
986 "\n",
987 " # Add the datasets available\n",
988 " pipe.add(cboe_vxn.open_.latest, 'vxn_open')\n",
989 "\n",
990 " # Set our pipeline screens\n",
991 " pipe.set_screen(is_liquid)\n",
992 " return pipe\n",
993 "\n",
994 "def initialize(context):\n",
995 " attach_pipeline(make_pipeline(), \"pipeline\")\n",
996 " \n",
997 "def before_trading_start(context, data):\n",
998 " results = pipeline_output('pipeline')"
999 ]
1000 },
1001 {
1002 "cell_type": "markdown",
1003 "metadata": {},
1004 "source": [
1005 "Now you can take that and begin to use it as a building block for your algorithms, for more examples on how to do that you can visit our <a href='https://www.quantopian.com/posts/pipeline-factor-library-for-data'>data pipeline factor library</a>"
1006 ]
1007 }
1008 ],
1009 "metadata": {
1010 "kernelspec": {
1011 "display_name": "Python 2",
1012 "language": "python",
1013 "name": "python2"
1014 },
1015 "language_info": {
1016 "codemirror_mode": {
1017 "name": "ipython",
1018 "version": 2
1019 },
1020 "file_extension": ".py",
1021 "mimetype": "text/x-python",
1022 "name": "python",
1023 "nbconvert_exporter": "python",
1024 "pygments_lexer": "ipython2",
1025 "version": "2.7.11"
1026 }
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1028 "nbformat": 4,
1029 "nbformat_minor": 0
1030 }